




'''simple docstring'''


import argparse
import logging
import os
import re

import tensorflow as tf

from transformers import (
    AutoConfig,
    AutoTokenizer,
    DataCollatorForLanguageModeling,
    PushToHubCallback,
    TFAutoModelForMaskedLM,
    create_optimizer,
)


UpperCAmelCase_					=   logging.getLogger(__name__)

UpperCAmelCase_					=   tf.data.AUTOTUNE


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       argparse.ArgumentParser(description='Train a masked language model on TPU.'      )
  parser.add_argument(
      '--pretrained_model_config'					,       type=a_					,       default='roberta-base'					,       help='The model config to use. Note that we don\'t copy the model\'s weights, only the config!'					,       )
  parser.add_argument(
      '--tokenizer'					,       type=a_					,       default='unigram-tokenizer-wikitext'					,       help='The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model\'s vocab size.'					,       )

  parser.add_argument(
      '--per_replica_batch_size'					,       type=a_					,       default=8					,       help='Batch size per TPU core.'					,       )

  parser.add_argument(
      '--no_tpu'					,       action='store_true'					,       help='If set, run on CPU and don\'t try to initialize a TPU. Useful for debugging on non-TPU instances.'					,       )

  parser.add_argument(
      '--tpu_name'					,       type=a_					,       help='Name of TPU resource to initialize. Should be blank on Colab, and \'local\' on TPU VMs.'					,       default='local'					,       )

  parser.add_argument(
      '--tpu_zone'					,       type=a_					,       help='Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.'					,       )

  parser.add_argument(
      '--gcp_project'					,       type=a_					,       help='Google cloud project name. Only used for non-Colab TPU nodes.'      )

  parser.add_argument(
      '--bfloat16'					,       action='store_true'					,       help='Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.'					,       )

  parser.add_argument(
      '--train_dataset'					,       type=a_					,       help='Path to training dataset to load. If the path begins with `gs://`'
      ' then the dataset will be loaded from a Google Cloud Storage bucket.'					,       )

  parser.add_argument(
      '--shuffle_buffer_size'					,       type=a_					,       default=2**18					,       help='Size of the shuffle buffer (in samples)'					,       )

  parser.add_argument(
      '--eval_dataset'					,       type=a_					,       help='Path to evaluation dataset to load. If the path begins with `gs://`'
      ' then the dataset will be loaded from a Google Cloud Storage bucket.'					,       )

  parser.add_argument(
      '--num_epochs'					,       type=a_					,       default=1					,       help='Number of epochs to train for.'					,       )

  parser.add_argument(
      '--learning_rate'					,       type=a_					,       default=1e-4					,       help='Learning rate to use for training.'					,       )

  parser.add_argument(
      '--weight_decay_rate'					,       type=a_					,       default=1e-3					,       help='Weight decay rate to use for training.'					,       )

  parser.add_argument(
      '--max_length'					,       type=a_					,       default=512					,       help='Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py'					,       )

  parser.add_argument(
      '--mlm_probability'					,       type=a_					,       default=0.15					,       help='Fraction of tokens to mask during training.'					,       )

  parser.add_argument('--output_dir'					,       type=a_					,       required=a_					,       help='Path to save model checkpoints to.'      )
  parser.add_argument('--hub_model_id'					,       type=a_					,       help='Model ID to upload to on the Hugging Face Hub.'      )

  __a								=       parser.parse_args()
  return args


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]      ):
  try:
    if args.tpu_name:
      __a								=       tf.distribute.cluster_resolver.TPUClusterResolver(
          args.tpu_name					,       zone=args.tpu_zone					,       project=args.gcp_project      )
    else:
      __a								=       tf.distribute.cluster_resolver.TPUClusterResolver()
  except ValueError:
    raise RuntimeError(
        'Couldn\'t connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or '
        '--gcp_project. When running on a TPU VM, use --tpu_name local.'      )

  tf.config.experimental_connect_to_cluster(a_      )
  tf.tpu.experimental.initialize_tpu_system(a_      )

  return tpu


def     SCREAMING_SNAKE_CASE							(   a_						:				List[Any]      ):
  __a								=       0
  for file in file_list:
    __a								=       file.split('/'      )[-1]
    __a								=       re.search(R'-\d+-(\d+)\.tfrecord'					,       a_      ).group(1      )
    __a								=       int(a_      )
    num_samples += sample_count

  return num_samples


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple					,       a_						:				Tuple					,       a_						:				str					,       a_						:				Union[str, Any]					,       a_						:				Tuple					,       a_						:				str=None      ):
  __a								=       count_samples(a_      )
  __a								=       tf.data.Dataset.from_tensor_slices(a_      )
  if shuffle:
    __a								=       dataset.shuffle(len(a_      )      )
  __a								=       tf.data.TFRecordDataset(a_					,       num_parallel_reads=a_      )
  # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
  __a								=       dataset.apply(tf.data.experimental.assert_cardinality(a_      )      )
  __a								=       dataset.map(a_					,       num_parallel_calls=a_      )
  if shuffle:
    assert shuffle_buffer_size is not None
    __a								=       dataset.shuffle(args.shuffle_buffer_size      )
  __a								=       dataset.batch(a_					,       drop_remainder=a_      )
  __a								=       dataset.map(a_					,       num_parallel_calls=a_      )
  __a								=       dataset.prefetch(a_      )
  return dataset


def     SCREAMING_SNAKE_CASE							(   a_						:				Tuple      ):
  if not args.no_tpu:
    __a								=       initialize_tpu(a_      )
    __a								=       tf.distribute.TPUStrategy(a_      )
  else:
    __a								=       tf.distribute.OneDeviceStrategy(device='/gpu:0'      )

  if args.bfloataa:
    tf.keras.mixed_precision.set_global_policy('mixed_bfloat16'      )

  __a								=       AutoTokenizer.from_pretrained(args.tokenizer      )
  __a								=       AutoConfig.from_pretrained(args.pretrained_model_config      )
  __a								=       tokenizer.vocab_size

  __a								=       tf.io.gfile.glob(os.path.join(args.train_dataset					,       '*.tfrecord'      )      )
  if not training_records:
    raise ValueError(f"No .tfrecord files found in {args.train_dataset}."      )
  __a								=       tf.io.gfile.glob(os.path.join(args.eval_dataset					,       '*.tfrecord'      )      )
  if not eval_records:
    raise ValueError(f"No .tfrecord files found in {args.eval_dataset}."      )

  __a								=       count_samples(a_      )

  __a								=       num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
  __a								=       steps_per_epoch * args.num_epochs

  with strategy.scope():
    __a								=       TFAutoModelForMaskedLM.from_config(a_      )
    model(model.dummy_inputs      )  # Pass some dummy inputs through the model to ensure all the weights are built
    __a						,							__a								=       create_optimizer(
        num_train_steps=a_					,       num_warmup_steps=total_train_steps // 20					,       init_lr=args.learning_rate					,       weight_decay_rate=args.weight_decay_rate					,       )

    # Transformers models compute the right loss for their task by default when labels are passed, and will
    # use this for training unless you specify your own loss function in compile().
    model.compile(optimizer=a_					,       metrics=['accuracy']      )

  def decode_fn(a_						:				Tuple      ):
    __a								=       {
        'input_ids': tf.io.FixedLenFeature(dtype=tf.intaa					,       shape=(args.max_length,)      ),
        'attention_mask': tf.io.FixedLenFeature(dtype=tf.intaa					,       shape=(args.max_length,)      ),
    }
    return tf.io.parse_single_example(a_					,       a_      )

  # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
  # use their methods in our data pipeline.
  __a								=       DataCollatorForLanguageModeling(
      tokenizer=a_					,       mlm_probability=args.mlm_probability					,       mlm=a_					,       return_tensors='tf'      )

  def mask_with_collator(a_						:				Dict      ):
    # TF really needs an isin() function
    __a								=       (
        ~tf.cast(batch['attention_mask']					,       tf.bool      )
        | (batch['input_ids'] == tokenizer.cls_token_id)
        | (batch['input_ids'] == tokenizer.sep_token_id)
    )
    __a						,							__a								=       data_collator.tf_mask_tokens(
        batch['input_ids']					,       vocab_size=len(a_      )					,       mask_token_id=tokenizer.mask_token_id					,       special_tokens_mask=a_					,       )
    return batch

  __a								=       args.per_replica_batch_size * strategy.num_replicas_in_sync

  __a								=       prepare_dataset(
      a_					,       decode_fn=a_					,       mask_fn=a_					,       batch_size=a_					,       shuffle=a_					,       shuffle_buffer_size=args.shuffle_buffer_size					,       )

  __a								=       prepare_dataset(
      a_					,       decode_fn=a_					,       mask_fn=a_					,       batch_size=a_					,       shuffle=a_					,       )

  __a								=       []
  if args.hub_model_id:
    callbacks.append(
        PushToHubCallback(output_dir=args.output_dir					,       hub_model_id=args.hub_model_id					,       tokenizer=a_      )      )

  model.fit(
      a_					,       validation_data=a_					,       epochs=args.num_epochs					,       callbacks=a_					,       )

  model.save_pretrained(args.output_dir      )


if __name__ == "__main__":
  UpperCAmelCase_					=   parse_args()
  main(args)









'''simple docstring'''


import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """segformer"""







   def __init__(						self   ,				UpperCamelCase=3   ,				UpperCamelCase=4   ,				UpperCamelCase=[2, 2, 2, 2]   ,				UpperCamelCase=[8, 4, 2, 1]   ,				UpperCamelCase=[32, 64, 160, 256]   ,				UpperCamelCase=[7, 3, 3, 3]   ,				UpperCamelCase=[4, 2, 2, 2]   ,				UpperCamelCase=[1, 2, 5, 8]   ,				UpperCamelCase=[4, 4, 4, 4]   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.0   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.02   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-6   ,				UpperCamelCase=256   ,				UpperCamelCase=255   ,				**UpperCamelCase   ,				)					->     int:
     super().__init__(**UpperCamelCase   )

     if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
       warnings.warn(
           'Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be'
           ' removed, as the behaviour will default to that of reshape_last_stage = True.'   ,				UpperCamelCase   ,				)

     __a								=       num_channels
     __a								=       num_encoder_blocks
     __a								=       depths
     __a								=       sr_ratios
     __a								=       hidden_sizes
     __a								=       patch_sizes
     __a								=       strides
     __a								=       mlp_ratios
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       classifier_dropout_prob
     __a								=       initializer_range
     __a								=       drop_path_rate
     __a								=       layer_norm_eps
     __a								=       decoder_hidden_size
     __a								=       kwargs.get('reshape_last_stage'   ,				UpperCamelCase   )
     __a								=       semantic_loss_ignore_index





class  __lowercase    (	__magic_name__			):
   _a    						=   version.parse("""1.11"""			)







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]   )







   @property
   def 		UpperCamelCase__      (						self   )					->     float:
     return 1e-4







   @property
   def 		UpperCamelCase__      (						self   )					->     int:
     return 12



